Memcached is a powerful distributed caching system widely used to accelerate dynamic web applications by alleviating database load. However, effective memory management is crucial to harness its full potential. Memcached allocates a fixed memory slab for caching, and when this memory is full, it must decide which items to evict to make room for new data. Properly configuring eviction policies and expiry times can significantly improve cache hit rates and reduce latency.

How Memcached Handles Eviction

By default, Memcached uses a Least Recently Used (LRU) eviction policy within each slab class. LRU removes the items that have not been accessed recently, assuming they are less valuable. Understanding how slab allocation and LRU eviction interact is essential for advanced tuning:

  • Slab Classes: Memcached divides memory into slab classes of fixed chunk sizes. Each slab class stores items of similar sizes to prevent fragmentation.
  • LRU Queue Management: Each slab class maintains its own LRU queue. When a slab is full, the least recently used items are evicted to free space.
  • Memory Fragmentation: Over time, inefficient allocation patterns can cause fragmentation, reducing effective memory usage.

Configuring Eviction Policies for Advanced Use Cases

While Memcached does not natively support multiple eviction policies beyond LRU, several configuration strategies can optimize eviction behavior:

  • LRU Segmentation Tuning: Adjusting parameters like -L (LRU crawler sleep time) and -v (verbosity) can help monitor and fine-tune the eviction process.
  • Slab Reassignment: Enabling slab reassignment (-o slab_reassign) dynamically redistributes memory among slab classes to reduce fragmentation and improve hit rates.
  • LRU Crawler Optimization: The LRU crawler can be configured to aggressively clean expired or stale keys, freeing space proactively.

For advanced setups, consider integrating cache warming strategies that pre-populate frequently accessed keys to avoid cold starts after eviction surges.

Setting Expiry Times: Balancing Freshness and Memory Pressure

Expiry time (TTL) defines how long a cached item remains valid. Optimizing expiry times requires balancing data freshness against memory constraints:

  • Short Expiry Times: Ensure data is fresh but increase cache churn, potentially causing higher eviction rates.
  • Long Expiry Times: Reduce cache misses but risk serving stale data and occupying memory unnecessarily.

Best practices for expiry configuration include:

  • Analyze application access patterns to assign TTLs based on usage frequency and data volatility.
  • Use tiered TTLs where critical, fast-changing data has shorter expiries while less volatile data persists longer.
  • Monitor cache hit/miss ratios and eviction stats to iteratively adjust TTLs.

Monitoring and Metrics to Inform Optimization

Effective optimization depends on continuous monitoring. Key Memcached statistics to track include:

  • evictions: Number of items evicted due to memory pressure.
  • curr_items: Current number of cached items.
  • get_hits and get_misses: Cache hit/miss ratio.
  • bytes: Memory used by cached items.
  • expired_unfetched: Expired items that were never accessed.

Tools like Memcached Top, Prometheus exporters, or built-in stats can help visualize these metrics. Use them to identify bottlenecks and validate the impact of configuration changes.

Advanced Techniques and Considerations

  • Using Touch Command: The touch command can refresh TTLs on frequently accessed keys, reducing premature eviction.
  • Client-side Expiry Handling: Some applications implement client-side logic to handle stale data, complementing Memcached’s TTL.
  • Hybrid Caching Architectures: Integrate Memcached with persistent caches or databases, using Memcached as a fast front layer with controlled expiry and eviction policies.

Conclusion

Optimizing Memcached memory usage revolves around mastering eviction policies and expiry configurations to balance performance, memory utilization, and data freshness. By understanding slab management, tuning LRU behaviors, and strategically setting TTLs, intermediate and advanced users can significantly enhance cache efficiency. Continuous monitoring and iterative adjustments ensure your Memcached deployment remains responsive and reliable under varying loads.

Master these configurations to unlock Memcached’s full potential as a scalable, high-performance caching layer in your infrastructure.